Master Thesis Foundation Model Knowledge Distillation into Deployable Perception Model Architectures for Automated Driving

Deep neural networks represent the state-of-the-art for 3D scene understanding in autonomous driving including occupancy prediction or object detection. However deployment constraints require compact models that can operate efficiently on target hardware under real-time conditions. While large foundation models have demonstrated exceptional capabilities across domains their computational demands make direct deployment in autonomous driving challenging.

This master thesis explores leveraging foundation models to enhance efficient deployable perception architectures for 3D occupancy prediction. The objective is to effectively transfer rich knowledge from large-scale foundation models into lightweight networks suitable for real-world autonomous driving deployment.

• During your thesis you will investigate and compare multiple knowledge transfer strategies.
• Using the Knowledge Distillation approach you will implement teacher-student frameworks where foundation models guide the training of compact deployable networks.
• With the Foundation Model Pretraining technique you will research and develop methodologies for leveraging foundation models as sophisticated feature extractors for pretraining target architecture components.
• Furthermore you will systematically implement evaluate and benchmark these approaches across accuracy computational efficiency and inference speed. The goal is to achieve substantial performance improvements while successfully bridging the gap between powerful foundation models and practical autonomous driving applications.

Qualifications :

Additional Information :

Start :  according to prior agreement

Duration : 6 months

Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records examination regulations and if indicated a valid work and residence permit.

Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.

Need further information about the job

Marc Sons (Functional Department)

#LI-DNI

Remote Work : No

Employment Type : Full-time

Key Skills

Python,C / C++,Fortran,R,Data Mining,Matlab,Data Modeling,Laboratory Techniques,MongoDB,SAS,Systems Analysis,Dancing

Experience : years

Vacancy : 1